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Understanding dynamic human mobility changes and spatial interaction patternsat different geographic scales is crucial for assessing the impacts ofnon-pharmaceutical interventions (such as stay-at-home orders) during theCOVID-19 pandemic. In this data descriptor, we introduce a regularly-updatedmultiscale dynamic human mobility flow dataset across the United States, withdata starting from March 1st, 2020. By analyzing millions of anonymous mobilephone users' visits to various places provided by SafeGraph, the daily andweekly dynamic origin-to-destination (O-D) population flows are computed,aggregated, and inferred at three geographic scales: census tract, county, andstate. There is high correlation between our mobility flow dataset and openlyavailable data sources, which shows the reliability of the produced data. Sucha high spatiotemporal resolution human mobility flow dataset at differentgeographic scales over time may help monitor epidemic spreading dynamics,inform public health policy, and deepen our understanding of human behaviorchanges under the unprecedented public health crisis. This up-to-date O-D flowopen data can support many other social sensing and transportationapplications.

Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic